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Pattern Recognition in Intensive Care Online Monitoring

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Computational Intelligence Processing in Medical Diagnosis

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 96))

Abstract

Clinical information systems can record numerous variables describing the patient’s state at high sampling frequencies. Intelligent alarm systems and suitable bedside decision support are needed to cope with this flood of information. A basic task here is the fast and correct detection of important patterns of change such as level shifts and trends in the data. We present approaches for automated pattern detection in online-monitoring data. Several methods based on curve fitting and statistical time series analysis are described. Median filtering can be used as a preliminary step to reduce the noise and to remove clinically irrelevant short term fluctuations.

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  1. R. Fried
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  2. U. Gather
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  3. M. Imhoff
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Editor information

Editors and Affiliations

  1. Technical University of Munich, Ismaninger Straße 22, 81675, München, Germany

    Manfred Schmitt

  2. Romanian Academy, Calea Victoriei 125, Bucharest, Romania

    Horia-Nicolai Teodorescu

  3. The Queens Elizabeth Hospital, Woodville Road, 5011, Woodville, Adelaide, South Australia, Australia

    Ashlesha Jain

  4. Bellevue Residential Care Centre, 5050, Bellevue Heights, Adelaide, South Australia, Australia

    Ajita Jain

  5. Julia Farr Services, Fullarton Road, 5063, Adelaide, South Australia, Australia

    Sandyha Jain

  6. Knowledge-Based Intelligent, Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, Adelaide, South Australia, Australia

    Lakhmi C. Jain

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Fried, R., Gather, U., Imhoff, M. (2002). Pattern Recognition in Intensive Care Online Monitoring. In: Schmitt, M., Teodorescu, HN., Jain, A., Jain, A., Jain, S., Jain, L.C. (eds) Computational Intelligence Processing in Medical Diagnosis. Studies in Fuzziness and Soft Computing, vol 96. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1788-1_6

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  • DOI: https://doi.org/10.1007/978-3-7908-1788-1_6

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2509-1

  • Online ISBN: 978-3-7908-1788-1

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